An empirical analysis of the liquidity effect on corporate bond yield spreads
Why this work is in the frame
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Bibliographic record
Abstract
Using the sample which consists of 139 corporate bonds from the year 2010 to 2017, it is found that liquidity effect is significant in determining corporate yield spreads, in addition, lower liquidity is related to a higher yield spread. Two liquidity measures, the bid-ask spread and the percentage of zero returns, are employed. For each liquidity measure in different models, the findings hold the same, after controlling for other determinants referring to the financial situations of firms, bond characteristics, and information from the Treasury bond market. In comparison to the percentage of zero returns, bid-ask spread performs better in explaining yield spreads. And the majority of yield spreads is explained by credit quality. The results are robust after controlling for potential endogeneity bias. The paper extends the work of some previous studies by researching the Canadian corporate bond market and has significant implications for the corporate bond valuation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it